]),
  ], panelHeight=10, startY=1)
  + g.util.grid.makeGrid([
    row.new('Push Information')
    + row.withPanels([
      panels.timeSeries.xdsPushes(
        'XDS Pushes', queries.xdsPushes, |||
          Rate of XDS push operations, by type. This is incremented on a per-proxy basis.
        |||
      ),
      panels.timeSeries.base(
        'Events', queries.pilotEvents, |||
          Size of each xDS push.
        |||

            {
               "datasource": {
                  "type": "prometheus",
                  "uid": "$datasource"
               },
               "expr": "sum by (cluster, namespace, job) (\n    rate(\n        process_cpu_seconds_total{\n            cluster=~\"$cluster\",\n            namespace=~\"$namespace\",\n            job=~\"$job\"\n        }\n    [$__rate_interval])\n)\n",
               "intervalFactor": 2,

{
  sum(query, by=[]):
    if std.length(by) == 0 then
      'sum (%s)' % [query]
    else
      'sum by (%s) (%s)' % [std.join(',', by), query],
  irate(query):
    'irate(%s[$__rate_interval])' % query,
  rate(query):
    'rate(%s[$__rate_interval])' % query,
  round(query, by=0.01):
    'round(%s, %s)' % [query, by],
  quantile(quantile, query):
    'histogram_quantile(%s, %s)' %[quantile, query],
  labels(metric_name, labels):

		"Average replication data transfer rate in bytes/sec")
	replicationCurrentActiveWorkersMD = NewGaugeMD(replicationCurrentActiveWorkers,
		"Total number of active replication workers")
	replicationCurrentDataTransferRateMD = NewGaugeMD(replicationCurrentDataTransferRate,
		"Current replication data transfer rate in bytes/sec")
	replicationLastMinuteQueuedBytesMD = NewGaugeMD(replicationLastMinuteQueuedBytes,

  int maximumSize;

  @Param("5000")
  int distinctKeys;

  @Param("4")
  int segments;

  // 1 means uniform likelihood of keys; higher means some keys are more popular
  // tweak this to control hit rate
  @Param("2.5")
  double concentration;

  Random random = new Random();

  LoadingCache<Integer, Integer> cache;

  int max;

  static AtomicLong requests = new AtomicLong(0);

	mockProm := mockPromAPI{
		cannedResponse: map[string]prometheus_model.Value{
			"sum(rate(istio_requests_total{destination_workload=~\"details.*\", destination_workload_namespace=~\".*\",reporter=\"destination\"}[1m0s]))": prometheus_model.Vector{ // nolint: lll
				&prometheus_model.Sample{Value: 0.04},
			},

	isReady.Store(false)
}

// installationThrottle is a small wrapper around a rate limitter. It aims to avoid excessive writes to CNI configuration,
// and detect if there is a loop of requests, typically caused by another component constantly reverting our work.
// Where possible, the remediate steps are logged.
type installationThrottle struct {
	limiter *rate.Limiter
	hits    int
	in      *Installer
}

package bandwidth

//go:generate msgp -file=$GOFILE -unexported

import (
	"context"
	"sync"
	"time"

	"golang.org/x/time/rate"
)

//msgp:ignore bucketThrottle Monitor

type bucketThrottle struct {
	*rate.Limiter
	NodeBandwidthPerSec int64
}

// Monitor holds the state of the global bucket monitor
type Monitor struct {
	tlock sync.RWMutex // mutex for bucket throttling

    row.new('Global Traffic')
    + row.withPanels([
      panels.timeSeries.statRps('Traffic Volume', queries.globalRequest, 'Total requests in the cluster'),
      panels.timeSeries.statPercent('Success Rate', queries.globalRequestSuccessRate, 'Total success rate of requests in the cluster'),
      panels.timeSeries.statRps('4xxs', queries.globalRequest4xx, 'Total 4xx requests in in the cluster'),

error rate, and request latency at p50, p90, and p99 percentiles. The
query results are printed to the console, organized by workload name.

All metrics returned are from server-side reports. This means that latencies
and error rates are from the perspective of the service itself and not of an
individual client (or aggregate set of clients). Rates and latencies are
calculated over a time interval of 1 minute.